Particle filter, also known as sequential Monte Carlo methods, is a type of recursive Bayesian filter commonly used in target tracking and state estimation problems in the field of signal processing and control systems. In particle filter, the state of a system is represented by a set of randomly generated particles or samples from the posterior distribution of the state variables. These particles are propagated through a dynamic model and weighted based on their likelihood of fitting the observed measurements. By resampling particles with higher weights and propagating them through the model, particle filter efficiently estimates the true state of the system even in the presence of nonlinearity, non-Gaussian noise, and model uncertainties. It is a powerful and flexible tool for state estimation and has been successfully applied in a wide range of applications including tracking vehicles, objects, and humans in video surveillance, localization in wireless sensor networks, and robot navigation.